6 ms·

Ambitious Artificial Intelligence Project Operating In Near-Secrecy For 30 Years

96 points by velodrome 12y ago

chazu 12y ago
This is pretty funny to me, because Cyc is actually a pretty high-profile project. Cursory searches on the topic of knowledge engineering return links to material on Cyc, not to mention that older AI textbooks mention Cyc as an example of a nascent large-scale knowledge engineering project.
TrainedMonkey 12y ago
Examining Company news section of the cyc website is way more interesting that the article: http://www.cyc.com/media-coverage http://www.cyc.com/media-coverage And here is how that page looked in 2003: http://web.archive.org/web/20031204204518/http://www.cyc.com/news.html http://web.archive.org/web/20031204204518/http://www.cyc.com... Quite a few of the publications are missing from most recent one. It is also worth noting that news page link was broken from 2003 until recently.
cwhy 12y ago
Low profile? Really? Or still in the winter of the old AI? Cyc more like hardcoded more than "taught" as stated in the article.
- gnudon 12y ago
  Agreed. Cyc is far from low profile. It was in Omni magazine back in the day - and it's a common example of (problematic) GOFAI approaches.
  - dllthomas 12y ago
    Presuming they've actually been diligently working away, it makes an interesting test of the "we just haven't done it hard enough" hypothesis.
  - zenbowman 12y ago
    Yeah it is hardly low profile. It was a very well funded project that promised the moon and failed to deliver despite absorbing a massive amount of DoD money. A lot of AI researchers at the lab I worked at weren't big fans as there was the sense that Cyc was a shining example of how money spent on symbolic AI was just wasted.
- rosser 12y ago
  Not really, no. I remember talking about Cyc in my AI classes in university, and I graduated in '97. Then again, Business Insider...
  - ganeumann 12y ago
    By "near secrecy" they mean "hasn't hired a PR firm."
SilasX 12y ago
Doug Lenat is, to me, the most confusing case of someone in AI. He programmed a general problem solver (Eurisko) to use (meta^n)-heuristics that solved a major strategy game, coming up wih a creative plan no human thought of, and yielding insights on the field of heuristics and the "Representation Language Language". ... and then "went dark", "officially" working on this tedious, brittle attempt to compile common sense into a graph, which yielded nothing in practical application. It's like we're in a movie and we're about to get a big reveal that he's really been using Eurisko to solve major untouchable problems.
- kolev 12y ago
  Since when people call him "Doug"? :) Eurisko had some amazing accomplishments, but some questioned their authenticity saying Douglas and students help it a bit. Anyway, Douglas Lenat is my all-times idol and I hope this prolonged "stealth mode" was the intermission of something great. Isn't Freebase.com his project as well?
  - candeira 12y ago
    > Since when people call him "Doug"? One datapoint: http://archive.wired.com/wired/archive/2.04/cyc-o.html http://archive.wired.com/wired/archive/2.04/cyc-o.html So for more than 20 years.
    - kolev 12y ago
      When I was reading about his work, it was the 80s, early 90s. :)
      - kolev 12y ago
        I'm not sure why I got downvoted, but, really, back at those times, maybe out of great respect, but he was "Douglas". :)
  - richardw 12y ago
    Since when people call him "Doug" He doesn't seem to mind: http://www.cyc.com/about/team/doug-lenat http://www.cyc.com/about/team/doug-lenat
kevin_thibedeau 12y ago
No it hasn't. Cyc has been widely reported on outside of academic circles for decades. It last got a lot a buzz when Watson was preparing to debut on Jeopardy.
sirseal 12y ago
Hmmm....smells like BS.
fiatmoney 12y ago
If you read "Why AM and Eurisko Appear To Work" [1] and the precursor papers it becomes clear that they relied heavily on humans annotating what counted as an "interesting" concept, both for training and for extraction of concepts to publicize. Not necessarily a bad thing (every supervised algorithm need supervision) but it was a little hype-y ("my robot has independently discovered natural numbers!"). They also ran into major tractability problems as the heuristics got more involved. Under the hood it was basically genetic algorithms over a meta-object protocol to extract well-scoring arrangements from ontologies, which is damn interesting, but combinatoric complexity bites you every time. Kenneth Haase published a couple of papers dealing with some of the issues in more depth. [1] http://eksl.isi.edu/files/library/Lenat_Brown-1984-why-AM-and-EURISKO-work.pdf http://eksl.isi.edu/files/library/Lenat_Brown-1984-why-AM-an...
Alupis 12y ago
30 years in the cooler? I would be very afraid of finally revealing 30 years of hard work, only to find out it's irrelevant now...or that we pursued in the wrong direction. Does not seem to be a very good way to go about revolutionizing any technology...
brianstorms 12y ago
Ha, near secrecy. Sheesh, Lenat is a well-known figure in the AI world and Cyc is a very well-known project that has been written about for years. I don't care if the BusinessInsider author is what, 28 years old, he should do some decent reporting and not use such a stupid headline.
- waterlesscloud 12y ago
  As soon as I saw the title, I knew who and what it would be about. :-)
- mtdewcmu 12y ago
  The sensational headline seems to have worked. I'm not sure if I've heard of this specific project, but to me it didn't sound all that novel. It reminds me of the semantic web.
mark_l_watson 12y ago
The free to use OpenCyc is fairly nice, I have been playing with it (occasionally)for years. My writeup of using the OWL/RDF dump: https://markwatson.com/blog/2013-08/opencyc-owl-stardog.html https://markwatson.com/blog/2013-08/opencyc-owl-stardog.html
chatmasta 12y ago
> "It's not done by any means, but it's useful." Is it? How do we know? You've been working on a product "in stealth" for 30 years, shown practically nobody outside your company, and provided us with nothing more than a vaporous description of its capabilities (in Businessinsider, no less). I'm left wondering not only how seriously I should be taking this claim, but also exactly what the claim even is. Artificial intelligence is hard. I did an internship at Numenta [1], where Jeff Hawkins is approaching AI from this same biological-first model. He hypothesized how a small subset of the brain works, constructed a model of it, and admirably hired dozens of engineers to build it. The guys behind the Numenta software are some of the best engineers I've met, with combined centuries of experience, and it's taken them almost ten years to get the software to its current, extremely primitive state. Right now, the model is implemented, and you can use the API to apply it to specific applications (predictive analytics, anomaly detection). But we are a long way off from the capability of applying it to "general input." The fact is, AI is not going to impress anyone until it can handle an equivalently general class of inputs to what a human can handle. And think about the inputs that humans receive. The five senses are just the beginning. They're the concrete inputs we receive from the world. In addition to them, there's a near infinite class of more abstract inputs that build on top of them. As humans, we interpret social cues, hormonal feedback, the emotions our body often inexplicably generates, and dozens of other "second-order" inputs that are derived from our fundamental sensory ones, but seem equally fundamental to us. Ok, sure. Maybe you can model the brain, and maybe it can respond to inputs from its sensory environment. But those inputs are the absolute lowest building block of the totality of input into our brain. We have an entire subconscience dedicated to processing those fundamental sensory inputs, and generating derivative ones for our consciousness to process. The difficulty in AI, in 2014, is modeling that subconscience. How do we go from basic environmental input to its infinitely more complex derivatives? How do we build a subconscience that our current AI can interpret as an input of its own? We are a long way off from this capability. Nobody is going to figure it out on their own, and neither is a company of a few dozen. To me, it seems incredibly wasteful to spend 30 years working on this product without revealing any of the journey. After all this time, what do you have to show for it? Basically nothing. Your AI can apparently complete tasks of a similar complexity to state of the art that's been developed in less than a decade. I want to see models, and I want the community to discuss them, problem solve with them, and build on them. Enough of this closed source, proprietary, snail's pace AI development. Software is constrained by computing limitations. Fundamentally, there can be no computational model that even approaches the complexity of a brain. At least not today. So why not forget about trying to build Skynet, and work with the community to further the field together? [1] www.numenta.org
jnord 12y ago
When I read this story the first thing that sprung to mind was Ted Nelson's Project Xanadu, described by Wired magazine as "the longest-running vaporware story in the history of the computer industry". As any entrepreneur will tell you, it is never a good idea to have long-running projects with few public deliverables. Projects like this need new blood, new ideas and continuous user validation in order to remain relevant.
- jfdixon 12y ago
  Xanadu is an unfair comparison. Cyc is deployed in real world applications[1][2]. [1] http://www.w3.org/2001/sw/sweo/public/UseCases/ClevelandClinic/ http://www.w3.org/2001/sw/sweo/public/UseCases/ClevelandClin... [2] http://videolectures.net/coinplanetdataschool2011_witbrock_cyc/ http://videolectures.net/coinplanetdataschool2011_witbrock_c...
tbenst 12y ago
I tested OpenCYC three years ago while working for a startup that did semantic tagging & recommendation of text-based content. Essentially, we would take something like: Gov. Rick Perry has said he will no longer wear cowboy boots, which some believe is part of an attempt to soften his gunslinging image as he considers another run for president. And map it to something more machine-readable: "Gov." -> http://dbpedia.org/page/State_(polity) (10% confidence)[1] "Rick Perry" -> http://dbpedia.org/page/Rick_Perry (95% confidence) "Rick Perry" -> http://sw.opencyc.org/concept/Mx4rM7N6iOeUSpGar4HaqXF3zg (95% confidence) "Cowboy boots" -> http://dbpedia.org/page/Cowboy_boot (75% confidence)[2] "President" ->http://dbpedia.org/page/President (90% confidence)[3] Let's dive into what you get from OpenCyc vs DBpedia (ontology sourced from Wikipedia). DBpedia: Tons of machine-readable information like Party, Alma Mater, birthday, spouse, etc. Extensive categorical links (dcterms:subject) like category:United_States_presidential_candidates,_2012. OpenCyc: Knows he's a politician affiliated with "Republican Party," "Democratic Party," "The Republican Party," and "The Democratic Party" I highlight only one example, but this is all over the place. Opencyc has duplicate terms where dbpedia/wikipedia does not. Opencyc has far less information. Opencyc has more incorrect information. This is inevitable when you consider the two approaches. Wikipedia has tens of thousands of people making connections and updating the resource, where opencyc relies more on scripts. Opencyc, and quite possibly Cyc, is already antiquated by Wikipedia. [1] Note that the confidence was estimated from heuristics I wrote based on how the ontologies were put together [2] OpenCyc typically does not have mappings for plurals, while wikipedia has a very convenient redirect system for string mappings [3] OpenCyc matches President with 60 concepts. Too much noise to do anything with
- jfdixon 12y ago
  I am not surprised by your results. However, the system is more robust than your experience would indicate. OpenCyc is a subset of ResearchCyc, which itself is a subset of (Full)Cyc. OpenCyc is primarily used for mapping between ontologies. It contains 239k concepts from ResearchCyc, but only the basic rules for definitional relationships between them. These relationships include part/whole, disjointness, etc. You mention DBPedia as being superior for your purpose, but I would counter that the two are complementary. There is a mapping between DBPedia and OpenCyc within the Linking Open Data cloud. In fact, it was one of the first ontologies contributed to the W3C's LOD initiative[1][2]. The concepts in OpenCyc are rigorously organized from most general (e.g. Thing) to more specific (e.g. board game). Each concept may have specific instances (e.g. Yahtzee, Trivial Pursuit, Scrabble, etc.) These primitives all live within a custom Lisp, where they may be reasoned over. DBPedia's structure arises naturally from user activity. It is organized primarily by Wikipedia's category system and includes individual pages. Unlike Wikipedia, the Cyc project does not aim to contain every instance of a concept. The relationships between concepts are what matter. Once one knows that something belongs to a given Cyc concept, one can leverage the system's knowledge to reason about it. OpenCyc's reasoning capability is limited by a lack of assertions (facts and rules) -- ResearchCyc's is not. ResearchCyc contains over 5 million assertions not present in OpenCyc. (Things like: water is wet, a dog is a mammal, mammals have hair, etc.) It also contains Natural Language tools not present in OpenCyc: parsers, taggers and more. With these tools, one can go from natural language to a formal logic representation. Or, given a formal representation generate natural language. These capabilities exist today in real world applications[3][4]. [1] http://lod-cloud.net http://lod-cloud.net [2] http://lod-cloud.net/versions/2007-10-08/lod-cloud.png http://lod-cloud.net/versions/2007-10-08/lod-cloud.png [3] http://videolectures.net/coinplanetdataschool2011_witbrock_cyc/ http://videolectures.net/coinplanetdataschool2011_witbrock_c... [4] http://videolectures.net/coinactivess2010_witbrock_lkc/ http://videolectures.net/coinactivess2010_witbrock_lkc/
deleted 12y ago
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pitchups 12y ago
Funny story - when I first started using Twitter a few years back, I had tweeted about some AI news. Pretty soon after that , I had a new follower that was trying to have a conversation with me - it was retweeting stuff randomly and sending some weird replies to me - it all seemed quite odd. Turned out that it was @cyc_ai - the Twitter handle of the the Cyc AI system, presumably trying hard to emulate a person - but failing unfortunately!
yottah 12y ago
If you look up their reviews by ex-employees you will find they are extremely negative, talk about lots of workplace bullying, insufficient resources and a vicious upper management that doesn't respect their workers.
deleted 12y ago
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savdi 12y ago
Human level artificial intelligence is a distance dream but can't say, virtual assistants like Google Now (http://www.google.com/landing/now/ http://www.google.com/landing/now/) and Braina (http://www.brainasoft.com/braina/ http://www.brainasoft.com/braina/) are already doing a decent job. May be we have to wait for 10 more years to achieve what is called AI-Complete or Strong AI.

96 points by velodrome 12y ago

chazu 12y ago

This is pretty funny to me, because Cyc is actually a pretty high-profile project. Cursory searches on the topic of knowledge engineering return links to material on Cyc, not to mention that older AI textbooks mention Cyc as an example of a nascent large-scale knowledge engineering project.

TrainedMonkey 12y ago

Examining Company news section of the cyc website is way more interesting that the article: http://www.cyc.com/media-coverage http://www.cyc.com/media-coverage And here is how that page looked in 2003: http://web.archive.org/web/20031204204518/http://www.cyc.com/news.html http://web.archive.org/web/20031204204518/http://www.cyc.com... Quite a few of the publications are missing from most recent one. It is also worth noting that news page link was broken from 2003 until recently.

cwhy 12y ago

Low profile? Really? Or still in the winter of the old AI? Cyc more like hardcoded more than "taught" as stated in the article.

gnudon 12y ago
Agreed. Cyc is far from low profile. It was in Omni magazine back in the day - and it's a common example of (problematic) GOFAI approaches.
- dllthomas 12y ago
  Presuming they've actually been diligently working away, it makes an interesting test of the "we just haven't done it hard enough" hypothesis.
- zenbowman 12y ago
  Yeah it is hardly low profile. It was a very well funded project that promised the moon and failed to deliver despite absorbing a massive amount of DoD money. A lot of AI researchers at the lab I worked at weren't big fans as there was the sense that Cyc was a shining example of how money spent on symbolic AI was just wasted.
rosser 12y ago
Not really, no. I remember talking about Cyc in my AI classes in university, and I graduated in '97. Then again, Business Insider...
- ganeumann 12y ago
  By "near secrecy" they mean "hasn't hired a PR firm."

SilasX 12y ago

Doug Lenat is, to me, the most confusing case of someone in AI. He programmed a general problem solver (Eurisko) to use (meta^n)-heuristics that solved a major strategy game, coming up wih a creative plan no human thought of, and yielding insights on the field of heuristics and the "Representation Language Language". ... and then "went dark", "officially" working on this tedious, brittle attempt to compile common sense into a graph, which yielded nothing in practical application. It's like we're in a movie and we're about to get a big reveal that he's really been using Eurisko to solve major untouchable problems.

kolev 12y ago
Since when people call him "Doug"? :) Eurisko had some amazing accomplishments, but some questioned their authenticity saying Douglas and students help it a bit. Anyway, Douglas Lenat is my all-times idol and I hope this prolonged "stealth mode" was the intermission of something great. Isn't Freebase.com his project as well?
- candeira 12y ago
  > Since when people call him "Doug"? One datapoint: http://archive.wired.com/wired/archive/2.04/cyc-o.html http://archive.wired.com/wired/archive/2.04/cyc-o.html So for more than 20 years.
  - kolev 12y ago
    When I was reading about his work, it was the 80s, early 90s. :)
    - kolev 12y ago
      I'm not sure why I got downvoted, but, really, back at those times, maybe out of great respect, but he was "Douglas". :)
- richardw 12y ago
  Since when people call him "Doug" He doesn't seem to mind: http://www.cyc.com/about/team/doug-lenat http://www.cyc.com/about/team/doug-lenat

kevin_thibedeau 12y ago

No it hasn't. Cyc has been widely reported on outside of academic circles for decades. It last got a lot a buzz when Watson was preparing to debut on Jeopardy.

sirseal 12y ago

Hmmm....smells like BS.

fiatmoney 12y ago

If you read "Why AM and Eurisko Appear To Work" [1] and the precursor papers it becomes clear that they relied heavily on humans annotating what counted as an "interesting" concept, both for training and for extraction of concepts to publicize. Not necessarily a bad thing (every supervised algorithm need supervision) but it was a little hype-y ("my robot has independently discovered natural numbers!"). They also ran into major tractability problems as the heuristics got more involved. Under the hood it was basically genetic algorithms over a meta-object protocol to extract well-scoring arrangements from ontologies, which is damn interesting, but combinatoric complexity bites you every time. Kenneth Haase published a couple of papers dealing with some of the issues in more depth. [1] http://eksl.isi.edu/files/library/Lenat_Brown-1984-why-AM-and-EURISKO-work.pdf http://eksl.isi.edu/files/library/Lenat_Brown-1984-why-AM-an...

Alupis 12y ago

30 years in the cooler? I would be very afraid of finally revealing 30 years of hard work, only to find out it's irrelevant now...or that we pursued in the wrong direction. Does not seem to be a very good way to go about revolutionizing any technology...

brianstorms 12y ago

Ha, near secrecy. Sheesh, Lenat is a well-known figure in the AI world and Cyc is a very well-known project that has been written about for years. I don't care if the BusinessInsider author is what, 28 years old, he should do some decent reporting and not use such a stupid headline.

waterlesscloud 12y ago
As soon as I saw the title, I knew who and what it would be about. :-)
mtdewcmu 12y ago
The sensational headline seems to have worked. I'm not sure if I've heard of this specific project, but to me it didn't sound all that novel. It reminds me of the semantic web.

mark_l_watson 12y ago

The free to use OpenCyc is fairly nice, I have been playing with it (occasionally)for years. My writeup of using the OWL/RDF dump: https://markwatson.com/blog/2013-08/opencyc-owl-stardog.html https://markwatson.com/blog/2013-08/opencyc-owl-stardog.html

chatmasta 12y ago

> "It's not done by any means, but it's useful." Is it? How do we know? You've been working on a product "in stealth" for 30 years, shown practically nobody outside your company, and provided us with nothing more than a vaporous description of its capabilities (in Businessinsider, no less). I'm left wondering not only how seriously I should be taking this claim, but also exactly what the claim even is. Artificial intelligence is hard. I did an internship at Numenta [1], where Jeff Hawkins is approaching AI from this same biological-first model. He hypothesized how a small subset of the brain works, constructed a model of it, and admirably hired dozens of engineers to build it. The guys behind the Numenta software are some of the best engineers I've met, with combined centuries of experience, and it's taken them almost ten years to get the software to its current, extremely primitive state. Right now, the model is implemented, and you can use the API to apply it to specific applications (predictive analytics, anomaly detection). But we are a long way off from the capability of applying it to "general input." The fact is, AI is not going to impress anyone until it can handle an equivalently general class of inputs to what a human can handle. And think about the inputs that humans receive. The five senses are just the beginning. They're the concrete inputs we receive from the world. In addition to them, there's a near infinite class of more abstract inputs that build on top of them. As humans, we interpret social cues, hormonal feedback, the emotions our body often inexplicably generates, and dozens of other "second-order" inputs that are derived from our fundamental sensory ones, but seem equally fundamental to us. Ok, sure. Maybe you can model the brain, and maybe it can respond to inputs from its sensory environment. But those inputs are the absolute lowest building block of the totality of input into our brain. We have an entire subconscience dedicated to processing those fundamental sensory inputs, and generating derivative ones for our consciousness to process. The difficulty in AI, in 2014, is modeling that subconscience. How do we go from basic environmental input to its infinitely more complex derivatives? How do we build a subconscience that our current AI can interpret as an input of its own? We are a long way off from this capability. Nobody is going to figure it out on their own, and neither is a company of a few dozen. To me, it seems incredibly wasteful to spend 30 years working on this product without revealing any of the journey. After all this time, what do you have to show for it? Basically nothing. Your AI can apparently complete tasks of a similar complexity to state of the art that's been developed in less than a decade. I want to see models, and I want the community to discuss them, problem solve with them, and build on them. Enough of this closed source, proprietary, snail's pace AI development. Software is constrained by computing limitations. Fundamentally, there can be no computational model that even approaches the complexity of a brain. At least not today. So why not forget about trying to build Skynet, and work with the community to further the field together? [1] www.numenta.org

jnord 12y ago

When I read this story the first thing that sprung to mind was Ted Nelson's Project Xanadu, described by Wired magazine as "the longest-running vaporware story in the history of the computer industry". As any entrepreneur will tell you, it is never a good idea to have long-running projects with few public deliverables. Projects like this need new blood, new ideas and continuous user validation in order to remain relevant.

jfdixon 12y ago
Xanadu is an unfair comparison. Cyc is deployed in real world applications[1][2]. [1] http://www.w3.org/2001/sw/sweo/public/UseCases/ClevelandClinic/ http://www.w3.org/2001/sw/sweo/public/UseCases/ClevelandClin... [2] http://videolectures.net/coinplanetdataschool2011_witbrock_cyc/ http://videolectures.net/coinplanetdataschool2011_witbrock_c...

tbenst 12y ago

I tested OpenCYC three years ago while working for a startup that did semantic tagging & recommendation of text-based content. Essentially, we would take something like: Gov. Rick Perry has said he will no longer wear cowboy boots, which some believe is part of an attempt to soften his gunslinging image as he considers another run for president. And map it to something more machine-readable: "Gov." -> http://dbpedia.org/page/State_(polity) (10% confidence)[1] "Rick Perry" -> http://dbpedia.org/page/Rick_Perry (95% confidence) "Rick Perry" -> http://sw.opencyc.org/concept/Mx4rM7N6iOeUSpGar4HaqXF3zg (95% confidence) "Cowboy boots" -> http://dbpedia.org/page/Cowboy_boot (75% confidence)[2] "President" ->http://dbpedia.org/page/President (90% confidence)[3] Let's dive into what you get from OpenCyc vs DBpedia (ontology sourced from Wikipedia). DBpedia: Tons of machine-readable information like Party, Alma Mater, birthday, spouse, etc. Extensive categorical links (dcterms:subject) like category:United_States_presidential_candidates,_2012. OpenCyc: Knows he's a politician affiliated with "Republican Party," "Democratic Party," "The Republican Party," and "The Democratic Party" I highlight only one example, but this is all over the place. Opencyc has duplicate terms where dbpedia/wikipedia does not. Opencyc has far less information. Opencyc has more incorrect information. This is inevitable when you consider the two approaches. Wikipedia has tens of thousands of people making connections and updating the resource, where opencyc relies more on scripts. Opencyc, and quite possibly Cyc, is already antiquated by Wikipedia. [1] Note that the confidence was estimated from heuristics I wrote based on how the ontologies were put together [2] OpenCyc typically does not have mappings for plurals, while wikipedia has a very convenient redirect system for string mappings [3] OpenCyc matches President with 60 concepts. Too much noise to do anything with

jfdixon 12y ago
I am not surprised by your results. However, the system is more robust than your experience would indicate. OpenCyc is a subset of ResearchCyc, which itself is a subset of (Full)Cyc. OpenCyc is primarily used for mapping between ontologies. It contains 239k concepts from ResearchCyc, but only the basic rules for definitional relationships between them. These relationships include part/whole, disjointness, etc. You mention DBPedia as being superior for your purpose, but I would counter that the two are complementary. There is a mapping between DBPedia and OpenCyc within the Linking Open Data cloud. In fact, it was one of the first ontologies contributed to the W3C's LOD initiative[1][2]. The concepts in OpenCyc are rigorously organized from most general (e.g. Thing) to more specific (e.g. board game). Each concept may have specific instances (e.g. Yahtzee, Trivial Pursuit, Scrabble, etc.) These primitives all live within a custom Lisp, where they may be reasoned over. DBPedia's structure arises naturally from user activity. It is organized primarily by Wikipedia's category system and includes individual pages. Unlike Wikipedia, the Cyc project does not aim to contain every instance of a concept. The relationships between concepts are what matter. Once one knows that something belongs to a given Cyc concept, one can leverage the system's knowledge to reason about it. OpenCyc's reasoning capability is limited by a lack of assertions (facts and rules) -- ResearchCyc's is not. ResearchCyc contains over 5 million assertions not present in OpenCyc. (Things like: water is wet, a dog is a mammal, mammals have hair, etc.) It also contains Natural Language tools not present in OpenCyc: parsers, taggers and more. With these tools, one can go from natural language to a formal logic representation. Or, given a formal representation generate natural language. These capabilities exist today in real world applications[3][4]. [1] http://lod-cloud.net http://lod-cloud.net [2] http://lod-cloud.net/versions/2007-10-08/lod-cloud.png http://lod-cloud.net/versions/2007-10-08/lod-cloud.png [3] http://videolectures.net/coinplanetdataschool2011_witbrock_cyc/ http://videolectures.net/coinplanetdataschool2011_witbrock_c... [4] http://videolectures.net/coinactivess2010_witbrock_lkc/ http://videolectures.net/coinactivess2010_witbrock_lkc/

deleted 12y ago

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pitchups 12y ago

Funny story - when I first started using Twitter a few years back, I had tweeted about some AI news. Pretty soon after that , I had a new follower that was trying to have a conversation with me - it was retweeting stuff randomly and sending some weird replies to me - it all seemed quite odd. Turned out that it was @cyc_ai - the Twitter handle of the the Cyc AI system, presumably trying hard to emulate a person - but failing unfortunately!

yottah 12y ago

If you look up their reviews by ex-employees you will find they are extremely negative, talk about lots of workplace bullying, insufficient resources and a vicious upper management that doesn't respect their workers.

deleted 12y ago

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savdi 12y ago

Human level artificial intelligence is a distance dream but can't say, virtual assistants like Google Now (http://www.google.com/landing/now/ http://www.google.com/landing/now/) and Braina (http://www.brainasoft.com/braina/ http://www.brainasoft.com/braina/) are already doing a decent job. May be we have to wait for 10 more years to achieve what is called AI-Complete or Strong AI.